from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from scipy.stats import ks_2samp
from sklearn.metrics import make_scorer, roc_auc_score, log_loss
from sklearn.model_selection import GridSearchCV
X, y = load_iris(return_X_y=True)

def pd(X,y):
    clf = LogisticRegression(random_state=0).fit(X, y)
    x=X[:2, :]
    res2=clf.predict_proba(x)









# grid = GridSearchCV(pl, param_grid=param_grid, scoring={'auc': roc_scorer, 'log': log_scorer}, refit='log')